





'''simple docstring'''







import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation






def 					_lowerCamelCase							(					lowercase		: int = 8		)      ->      Union[str, Any]:
				_a				      =					ascii_letters + digits + punctuation
				return "".join(secrets.choice(lowercase		) for _ in range(lowercase		)		)






def 					_lowerCamelCase							(					lowercase		: str       ,				lowercase		: int		)      ->      Optional[int]:
				i -= len(lowercase		)
				_a				      =					i // 3
				_a				      =					i % 3
				# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
				#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
				_a				      =					(
				    chars_incl
				    + random(lowercase       ,				quotient + remainder		)
				    + random(lowercase       ,				lowercase		)
				    + random(lowercase       ,				lowercase		)
				)
				_a				      =					list(lowercase		)
				shuffle(lowercase		)
				return "".join(lowercase		)

				# random is a generalised function for letters, characters and numbers






def 					_lowerCamelCase							(					lowercase		: str       ,				lowercase		: int		)      ->      Union[str, Any]:
				return "".join(secrets.choice(lowercase		) for _ in range(lowercase		)		)






def 					_lowerCamelCase							(					lowercase		: List[Any]       ,				lowercase		: List[Any]		)      ->      List[Any]:
				pass  # Put your code here...






def 					_lowerCamelCase							(					lowercase		: List[Any]       ,				lowercase		: int		)      ->      Union[str, Any]:
				pass  # Put your code here...






def 					_lowerCamelCase							(					lowercase		: Optional[Any]       ,				lowercase		: Dict		)      ->      int:
				pass  # Put your code here...






def 					_lowerCamelCase							(					lowercase		: str       ,				lowercase		: int = 8		)      ->      Any:




				if len(lowercase		) < min_length:
								# Your Password must be at least 8 characters long
								return False

				_a				      =					any(char in ascii_uppercase for char in password		)
				_a				      =					any(char in ascii_lowercase for char in password		)
				_a				      =					any(char in digits for char in password		)
				_a				      =					any(char in punctuation for char in password		)

				return upper and lower and num and spec_char
				# Passwords should contain UPPERCASE, lowerase
				# numbers, and special characters






def 					_lowerCamelCase							(					)      ->      Union[str, Any]:
				_a				      =					int(input("Please indicate the max length of your password: "		).strip()		)
				_a				      =					input(
				    "Please indicate the characters that must be in your password: "		).strip()
				print("Password generated:"       ,				password_generator(lowercase		)		)
				print(
				    "Alternative Password generated:"       ,				alternative_password_generator(lowercase       ,				lowercase		)       ,				)
				print("[If you are thinking of using this passsword, You better save it.]"		)


if __name__ == "__main__":
					main()







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  ["RoFormerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RoFormerForCausalLM",
           "RoFormerForMaskedLM",
           "RoFormerForMultipleChoice",
           "RoFormerForQuestionAnswering",
           "RoFormerForSequenceClassification",
           "RoFormerForTokenClassification",
           "RoFormerLayer",
           "RoFormerModel",
           "RoFormerPreTrainedModel",
           "load_tf_weights_in_roformer",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRoFormerForCausalLM",
           "TFRoFormerForMaskedLM",
           "TFRoFormerForMultipleChoice",
           "TFRoFormerForQuestionAnswering",
           "TFRoFormerForSequenceClassification",
           "TFRoFormerForTokenClassification",
           "TFRoFormerLayer",
           "TFRoFormerModel",
           "TFRoFormerPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FlaxRoFormerForMaskedLM",
           "FlaxRoFormerForMultipleChoice",
           "FlaxRoFormerForQuestionAnswering",
           "FlaxRoFormerForSequenceClassification",
           "FlaxRoFormerForTokenClassification",
           "FlaxRoFormerModel",
           "FlaxRoFormerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






